Dummy fill for integrated circuits

ABSTRACT

A method and system are described to reduce process variation as a result of the electrochemical deposition (ECD), also referred to as electrochemical plating (ECP), and chemical mechanical polishing (CMP) processing of films in integrated circuit manufacturing processes. The described methods use process variation and electrical impact to direct the insertion of dummy fill into an integrated circuit.

In fabricating integrated circuits, interconnect film thicknessuniformity, dishing and erosion are dependent on variation in layoutpatterns (e.g. material density, linewidth and linespace). Surfacenon-uniformity often leads to subsequent manufacturability and processintegration issues. These pattern dependencies may also affect deviceperformance by introducing variation in capacitance and resistancedepending on the location of a given structure on the device.

Film thickness variation in chemical mechanical polishing (CMP)processes can be separated into various components: lot-to-lot,wafer-to-wafer, wafer-level, and die-level. Oxide thickness variationdue to CMP is mainly due to differences in layout patterns on the chip.Often, the most significant component is the pattern dependent ordie-level component. The oxide is generally polished until all areas onthe chip have been planarized. However, differences in the underlyingmetal pattern result in variation in the post CMP oxide thickness, eventhough a locally planar layer of oxide is achieved with CMP.

For oxide polishing, the major source of variation is caused by withindie pattern density. Pattern density is defined as the ratio of raisedoxide area divided by the total area of the region. The region may betaken as a square with the length of the sides equal to some length, theplanarization length. The planarization length is usually determined byprocess factors such as the type of polishing pad, CMP tool, slurrychemistry, etc. The effective pattern density may be computed for eachlocation on the die by filtering the designed layout densities, often byusing various two-dimensional filters of densities around the givenlocation.

For metal polishing in a damascene CMP process, other physical layouteffects such as the linewidth and linespace may also be required. Twoeffects known as dishing and erosion result from metal damascene CMP.Dishing is measured as the difference in metal thickness at the edge ofa line and its center. Erosion is defined as the difference in oxidethickness above a metal line, typically within an array of lines, to anadjacent unpatterned region. FIG. 1A shows the desired result of CMP ina damascene process where the copper features, 1 and 2, in the oxidefield, 4, meet the desired wafer surface, 3. FIG. 1B shows the effectsof the layout pattern on metal thickness variation in a damascene CMPprocess where the desired wafer surface, 5, does not match the actualwafer surface, 6. FIG. 1B shows the effects of Cu CMP dishing, 7, in awide line as well as the effects of erosion, 8, in an array of finepitch lines. These figures illustrate that other physical layoutparameters, in addition to pattern density, are required to predict thevariation in a damascene CMP process.

Dummy fill structures can be added to improve process uniformity. Addingmetal dummy fill increases the pattern density since density is definedas the amount of metal divided by the total area within a given region.Conversely, adding oxide dummy removes sections of the copper line anddecreases the pattern density. The addition of fill can also alter otherparameters such as linewidth and linespace. If dummy metal is insertedbetween two parallel lines, the linespace changes for both of thoselines. Similarly, if oxide dummy is inserted within a wire, itseffective linewidth is changed. By modifying the existing layout throughthe addition of dummy fill, physical parameters such as pattern density,linewidth, and linespace are changed. Since metal or oxide filmthickness non-uniformity resulting from CMP is dependent on thesephysical parameters, the addition or removal of metal alters thephysical characteristics of the designed layout. Therefore, the additionof metal or oxide fill based on process models can reduce the filmthickness non-uniformity.

Dummy fill is a method of improving film thickness uniformity inintegrated circuits through the addition or removal of existingstructures. The following two figures illustrate two types of dummyfill, metal and oxide. FIG. 2 illustrates the use of metal dummy fill.FIG. 2A shows a series of metal lines, 10, in an oxide layer, 9, with alarge oxide field region in the middle, 11, that is available for dummyfill. One goal of dummy fill is to achieve uniform pattern densityeverywhere so that deposition and polishing process result in a planarfilm thickness. As such, this area would be selected as available fordummy fill. This region is particularly attractive in that it is ofsufficient distance from electrically conducting lines and may minimizethe impact of dummy fill on capacitance. In FIG. 2B, metal dummy fill,14, has been placed in the oxide field area, 13, to raise the density ofthis region, while the metal dummy fill remains electrically isolatedfrom the conducting regions, 12, around it.

FIG. 3 illustrates the addition of oxide dummy fill in a metal field. InFIG. 3A, metal field region, 15, has a large area, 16, available foroxide dummy fill. In FIG. 3B, oxide dummy fill (also referred to asmetal slotting), 18, is added to the metal area, 17, raising the densityof raised area of the region and subsequently improving the polishinguniformity (reducing the film thickness variation) of this region. Theterm “dummy fill area” is used to refer to the area where dummy fill isadded and the term “dummy fill structures” is used to refer to the newobjects that are embedded within that area.

Dummy fill may be placed using a particular placement pattern to reducethe impact of its presence within either a conducting or insulatingstructure. FIG. 4 shows three different dummy fill patterns. The firstpanel, FIG. 4A shows symmetric fill structures that are commonly usedwhen oxide fill is placed in metal. The symmetric pattern promotes theflow of current through the metal region. The second and third panels,FIG. 4B and FIG. 4C, show asymmetric patterns that are commonly usedwhen metal fill (i.e. a conducting material) is placed in an insulatingmaterial (e.g. oxide). The asymmetric nature retards the capacitivecoupling between adjacent signal lines more than a symmetric pattern,resulting in reduced cross-talk noise. Designers desire that theaddition of dummy fill not alter the desired electrical performance.However, the addition of dummy structures may unintentionally affect theelectrical properties and degrade chip performance. Important factorsmust be considered for effective dummy fill. These factors includeprocess effects, electrical effects, and placement impact.

The electrical performance of a circuit can be determined by theelectrical characteristics of its interconnect, and the interconnect isoften the limiting factor in high performance designs. These electricalparameters include the interconnect resistance and capacitance. Circuitperformance metrics such as signal delay, clock skew, and crosstalknoise are functions of the interconnect resistance and capacitance. Theinterconnect resistance is a function of the wire resistivity, metalthickness, interconnect length, and linewidth. The interconnectcapacitance is a function of the metal thickness, interconnect length,linewidth, linespace, and dielectric constant of the insulator (oxide)between the wires. Note that the geometry of the interconnect structuresimpact their electrical properties. Therefore, any variation in thegeometry, such as the insertion of dummy fill or slots, may also affectthe electrical performance metrics.

The addition of dummy fill can result in unwanted electrical effects.Adding dummy features alters the effective pattern density andlinespace. Removing features (oxide fill) alters the effective patterndensity and linewidth. The impact of fill depends on the designedinterconnect structure neighboring the fill (for metal dummy) or thedesigned interconnect structure itself (for oxide dummy). Adding metalfill modifies the coupling capacitance (C) between neighboringinterconnects. Adding oxide dummy modifies the coupling capacitance (C)and interconnect resistance (R). The relative impact depends on thedimensions of the interconnect structures. The level of variations in Rand C determine how the circuit is affected.

Design rules can be constructed such that an acceptable level ofvariation tolerance is set for the interconnect RC variation.Alternatively, design rules can be set to allow a certain tolerancelevel for the circuit performance metrics such as signal delay, clockskew, or crosstalk noise. These performance metrics are normallyfunctions of the interconnect RC. The total interconnect capacitance isheavily dependent on neighboring structures. These structures can bedescribed as a canonical set where an object or class of objects isuniquely identified or standardized within a set of features (such aslinewidth, linespace or density) related to process variation.Therefore, a dummy fill strategy should account for these electricaleffects in addition to the process uniformity specifications relative tothese features.

A short flow damascene process using ECD and CMP is shown in FIGS. 5 &6. FIG. 5A illustrates step 1, where trenches, 19, are created in oxidefor the interconnect structures using lithography and etching. FIG. 5Bshows the early stage of step 2 where electroplating is used to fill thetrench, 23, in field oxide, 20, from time T0, 21, to T2, 22. FIG. 6Ashows the end at time Tf, 26, of step 2 where electroplating fills thetrench, 27, in the field oxide, 24. FIG. 6B illustrates how CMP is usedto remove the copper such that the trench, 28, is planar with the fieldoxide, 29.

Once the copper is deposited, it must be polished until all of thecopper above the field regions is cleared. CMP is the leading method ofcopper removal and planarization in semiconductor manufacturingprocesses. Differences in the structures and their surroundings resultin variable polish rates across the chip. To guarantee that there are noshorts between interconnects, over-polishing is done until all thecopper is cleared above the field oxide. This results in metal thicknessvariation (see FIG. 4). Another application of dummy fill is to modifythe interconnect structures and surrounding areas to reduce thevariation. This can be done by adding metal dummy fill between theinterconnect regions or removing metal from the existing interconnect.As such, the layout can be altered from its original design by addingadditional features (metal dummy fill) or removing sections of existingfeatures (slotting with oxide dummy fill). This improves processuniformity but can adversely affect the electrical performance of thechip. Therefore, the goal is to fill the layout in a way that reducesthe process variation while preserving the original intended functionsof the circuit.

SUMMARY

In general, in one aspect, the invention features, based on electricalimpact analysis and a pattern dependent model of a chemical mechanicalpolishing process, generating a strategy for placement of dummy fill inthe process, and using the pattern dependent model and the electricalimpact analysis to evaluate the expected results of the dummy fill to beplaced, the use of the model and the electrical impact analysis beingembedded as part of the generation of the dummy fill placement strategy.

In general, in another aspect, the invention features, based on anelectrical impact analysis and a pattern dependent model of a chemicalmechanical polishing process, generating a strategy for placement ofdummy fill in the process, and using the pattern dependent model and theelectrical impact analysis to evaluate the expected results of the dummyfill to be placed, the fabrication process for which the strategy isbeing generated comprising other than an oxide chemical mechanicalpolishing process.

In general, in another aspect, the invention features, based on apattern dependent model of a chemical mechanical polishing process,generating a strategy for placement of dummy fill in the process, andusing the pattern dependent model to evaluate the expected results ofthe dummy fill to be placed, the fabrication process for which thestrategy is being generated comprising two or more stages offabrication.

In general, in another aspect, the invention features, based on apattern dependent model of a semiconductor fabrication process,generating a strategy for placement of dummy fill in the process, andusing the pattern dependent model to evaluate the expected results ofthe dummy fill to be placed, the fabrication process for which thestrategy is being generated comprising a polishing or planarizationprocess in which more than one material is removed.

Implementations of the invention may include one or more of thefollowing features.

A server is operated to provide dummy fill generation functions for asemiconductor design, and a user at a client is enabled to operatethrough a web browser to develop the dummy fill placement strategy. Theserver is local to the user. The server is remote from the user. Adesign to which the dummy fill strategy has been applied is analyzed,the design is adjusted based on the analysis, the analyzing andadjusting steps are iterated, and an integrated circuit manufacturedaccording to the adjusted design is certified to be within predefinedphysical and electrical parameters. The two stages comprise two or moreprocesses. The two stages comprise two or more steps of a singleprocess. The two stages comprise deposition and chemical mechanicalpolishing.

The generating of a strategy includes generating dummy fill rules. A setof hierarchical cell placements is defined for dummy fill, and a size ofan electronic layout file to which dummy fill is added is reduced byusing the hierarchical cell placements.

The dummy fill generation is performed by a user through a web browserand a web server. The web server is local to the user. The web server isremote from the user. The process comprises a damascene process. Thestrategy for placement of dummy fill includes determining the size andplacement of dummy fill.

The fabrication process comprises a formation of a low-K interlayerdielectric. The fabrication process comprises chemical vapor depositionor spin-on of the low-K dielectric.

Generating the dummy fill strategy includes dividing a semiconductordesign into grids. Generating the dummy fill strategy also includesextracting local pattern densities for a semiconductor design for eachof the grids. Generating the dummy fill strategy also includesextracting local line width for a semiconductor design for each of thegrids. Generating the dummy fill strategy also includes extracting localline spacing for a semiconductor design for each of the grids.Generating the dummy fill strategy also includes computing an effectivepattern density for each grid. Models are used for computing filmthickness non-uniformity with respect to a semiconductor design forwhich the dummy fill strategy is being generated. A variation in filmthickness is determined. Coordinates of all objects within each of thegrids are derived. At least one of line width, line space, length, andbounding box is generated with respect to each of the objects. The dummyfill strategy includes adding dummy fill in empty areas of each of thegrids. The dummy fill includes slots in objects. A local density isrecomputed in each of the grids after adding dummy fill. An effectivepattern density is recomputed for each of the grids after adding dummyfill. The dummy fill strategy is based on criteria for electricalparameter variation tolerances for at least one of the following:capacitance and resistance, sheet resistance, outputs delay, skew,voltage drop, drive current loss, dielectric constant or crosstalknoise. The effective pattern density is computed based on a polishingprocess planarization length. The effective pattern density is computedusing an elliptically weighted window or other filter. Dummy fill rulesbased on electrical design guidelines are generated dynamically with achange in technology or design parameters. An effective pattern densityis generated dynamically with a change in a process planarizationlength. The fabrication process comprises lithography or electrochemicaldeposition or copper chemical mechanical polishing.

Pattern dependencies are extracted from a layout of the semiconductor.The layout dependencies include dependencies with respect to linespacing, line width or line density. Patterned test wafers or testsemiconductor devices are used to calibrate a pattern dependent modelwith respect to a preselected tool or process recipe, and based on apattern dependent model of a semiconductor fabrication process, thestrategy for placement of dummy fill in the process is generated. Acalibrated pattern dependent model is used to map pattern dependentfeatures to wafer-state parameters such as resulting film thickness,film thickness variation, dishing, erosion and electrical parameterssuch as sheet resistance, resistance, capacitance, crosstalk noise,voltage drop, drive current loss, dielectric constant and effectivedielectric constant, and based on the pattern dependent model, thestrategy for placement of dummy fill in a fabrication process isgenerated.

A cost function is used to measure an impact of dummy fill modificationon process induced wafer state and electrical parameter variation. Animpact of the dummy fill generated by the strategy on process variationis predicted. Based on a combination of more than one pattern dependentmodel and cost function, a strategy is generated for placement of dummyfill in a process that optimizes full-chip wafer-state and electricalparameters. Based on predicted or simulated wafer state and electricalparameters, dummy fill rules are generated for use in dummy fillplacement in a semiconductor fabrication process. The dummy fill rulesinclude dummy fill sizing. The dummy fill rules include dummy fillplacement. The dummy fill rules include dummy fill hierarchical cellcreation and management. Dummy fill functions are provided to generatethe dummy fill strategy, and the functions are used to automaticallymodify GDS-format electronic layout files for a semiconductor device.

At an internet server, a layout file for a semiconductor device isreceived from a client, dummy fill modifications to the layout file aregenerated at the server, and the dummy fill modified layout file isreturned from the server to the client. A service is provided thatenables a user to interactively configure a dummy fill applicationrunning on the server, and enables the user to generate dummy fillinformation using the dummy fill application. A service is madeavailable to a user on a network that enables the user to verify dummyfill information with respect to a semiconductor design and afabrication process. The dummy fill information that is verifiedincludes at least one of a dummy fill pattern, a dummy fill strategy, ora dummy fill representation. The dummy fill information is verified withrespect to a single interconnect level of the semiconductor design. Thedummy fill information is verified with respect to multiple interconnectlevels of the semiconductor design.

Dummy fill objects are sized, and a dummy fill pattern of the objects iscreated for one or more interconnect levels of the semiconductor design.The dummy fill information comprises dummy fill rules. The patternincludes oxide or metal dummy fill objects. The objects of the dummyfill pattern are placed to minimize full-chip film thickness variation.The objects of the dummy fill pattern are placed to minimize full-chipvariation in electrical parameters. The electrical parameters compriseat least one of sheet resistance, resistance, capacitance, crosstalknoise, voltage drop, drive current loss, and effective dielectricconstant. The GDS files are modified to improve uniformity andelectrical performance of the semiconductor device. The processcomprises a damascene process flow.

a web-based application comprised of web services is made available to auser on a network that enables the user to verify dummy fill informationwith respect to a semiconductor design and a fabrication process. Thedummy fill placement strategy includes using dummy fill objects toimprove a structural integrity of low-K dielectric features. The dummyfill placement strategy includes using dummy fill objects to maintain orimprove an effective dielectric constant of low-K dielectric features.The effective dielectric constant is maintained through all steps of adamascene process flow. The dummy fill placement strategy includes usingdummy fill objects to facilitate integration of low-k dielectricmaterials into a damascene process flow.

A library is maintained of semiconductor dummy fill information, and thelibrary is made available for use in connection with generating dummyfill placement specifications, and the library is updated with changeddummy fill information. Calibration information is stored with respectto at least one of the following: process tools, recipes, and flows, andupdating the calibration information to reflect changes in the processtools, recipes or flows. The calibration information is used ingenerating a dummy fill strategy. A selection is made among processtools, recipes and flows from calibration database based upon desireddummy fill characteristics. A user is enabled to obtain a dummy fillstrategy for a semiconductor design using a single click of a userinterface device through a user interface. A user is enabled to obtain adummy fill strategy for a semiconductor design over the internet usingweb services.

Other advantages and features will become apparent from the followingdescription and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates the ideal wafer surface profile resulting from CMPused in a copper damascene process.

FIG. 1B illustrates a more realistic case where dishing occurs in metallines and erosion occurs in surrounding oxide regions.

FIG. 2A illustrates an oxide field region containing copper lines and alarge open oxide field area in the middle.

FIG. 2B illustrates the addition of metal dummy fill to raise theeffective density of metal and promote better film thickness uniformityafter CMP.

FIG. 3A illustrates a large metal field region.

FIG. 3B illustrates the addition of oxide dummy fill to the metalregion.

FIG. 4A illustrates a symmetric dummy fill pattern.

FIG. 4B illustrates an asymmetric dummy fill pattern in one direction.

FIG. 4C illustrates an asymmetric dummy fill pattern in two directions.

FIG. 5A illustrates the creation of a trench in oxide for electroplating(ECD) fill

FIG. 5B illustrates the initial stages of copper deposition into thetrench using ECD

FIG. 6A illustrates the final stages of copper deposition into thetrench

FIG. 6B illustrates the use of CMP to polish the copper overburden

FIG. 7 illustrates the computational flow of the complete dummy fillmethod

FIG. 8A illustrates the variation in ECD deposition thickness as aresult of pattern dependencies, such wide and narrow lines.

FIG. 8B illustrates the use of oxide dummy posts to achieve uniform ECDfilm thickness.

FIG. 9A illustrates the steps involved in layout extraction.

FIG. 9B illustrates a continuation of the steps involved in layoutextraction.

FIG. 10A illustrates the structures neighboring a given object, A.

FIG. 10B illustrates the distance between objects A and B.

FIG. 10C illustrates the distance between closer and farther neighboringobjects.

FIG. 10D illustrates how the grid boundary may be considered aneighboring object.

FIG. 11 illustrates the relationship between spatial regions across thechip and the creation of a layout extraction table.

FIG. 12A illustrates the use of product wafers in calibrating a tool fora particular recipe.

FIG. 12B illustrates the use of test wafers in calibrating a tool for aparticular recipe.

FIG. 13A illustrates how a calibration is used to map layout features tofilm thickness variation.

FIG. 13B illustrates the use of the calibration mapping to predict filmthickness variation for a new IC design.

FIG. 13C illustrates how wafer-state parameters, such as film thicknessvariation, can be used to predict electrical parameters.

FIG. 14 describes the flow of steps used to calibrate a process step andgenerate a model.

FIG. 15 describes the flow of steps used to generate a prediction usinga calibrated model.

FIG. 16 describes the flow of steps used to generate dummy fill rulesand tables

FIG. 17 provides a sample dummy rule table, showing maximum dummy filllinewidth as a function of interconnect linewidth and linespace.

FIG. 18 describes the flow of steps used in sizing and placing dummyfill

FIG. 19 describes the detailed flow of steps used in placing dummy fillobjects within a block.

FIG. 20A provides an example of dummy fill sizing rules

FIG. 20B provides an example of dummy fill pattern generation rules

FIG. 21A illustrates a symmetric dummy fill pattern

FIG. 21B illustrates how another symmetric dummy fill pattern can havethe same effective density but use different fill object sizes.

FIG. 22A illustrates how a 1×2 cell can be used to generate a 2×4 cell.

FIG. 22B illustrates how a 1×2 cell can be used to generate a 4×4 cell.

FIG. 23 illustrates how a 4×4 cell can be represented by 8 objects.

FIG. 24 describes the flow of steps used to create a cell hierarchy

FIG. 25 describes modifications to the dummy fill method described inFIG. 7 to use dummy fill methods with low-k dielectric films.

FIG. 26 illustrates the computation architecture used to implement thedummy fill methods, also referred to as the dummy fill system.

FIG. 27A illustrates a stand-alone implementation where the dummy fillsystem resides on one computer.

FIG. 27B illustrates a client-server implementation of the dummy fillsystem.

FIG. 28 illustrates the extension of the client-server implementation toinclude external components across a network.

FIG. 29 illustrates a general client-server dummy fill framework thatuses the internet, extranet or intranet.

FIG. 30 illustrates the preferred computational framework for the dummyfill system using a client-server framework with web services.

FIG. 31 illustrates how web services may be used to dynamically build adummy fill web applications tailored to a particular user.

FIG. 32 illustrates series operation of the dummy fill system where allof the IC components are added to a particular layout before it issubmitted to the dummy fill system.

FIG. 33 illustrates real-time use of the dummy fill system to analyzeand place dummy fill as components are added to the layout.

FIG. 34 provides a screenshot of the dummy fill system layout managerGUI, which manages a users layouts and layout extraction.

FIG. 35A provides the result of a density extraction using the dummyfill system.

FIG. 35B provides the result of a linewidth extraction using the dummyfill system.

FIG. 36A provides the results of the dummy fill system for metal dummyfill.

FIG. 36B provides the results of the dummy fill system for oxide dummyfill.

FIG. 37 provides the results of the dummy fill system used to placemetal dummy fill with a size and pattern selected by the system tominimize electrical impact.

FIG. 38 provides a screenshot of tool type information available underthe manufacturing component of the dummy fill system.

DETAILED DESCRIPTION

We describe a method of adding dummy fill to reduce process variationscaused by dependencies in the electrochemical deposition and subsequentchemical mechanical polishing of interconnect features used insemiconductor devices. The variation in wafer quality (e.g. filmthickness variation and surface topography variation such as dishing anderosion) and electrical parameters (resistance, capacitance, and noise)are modeled and simulated using semi-physical process models that may becalibrated to a particular process and tool for each step in a sequenceof one or more steps within a process flow. Dummy fill structures areplaced in the layout to improve thickness and surface topographyuniformity of the manufactured wafer while maintaining the electricalparameters at the intended or designed values. The added structures areplaced in such a way as to: modify the design layout parameters such aseffective pattern density, maximum and minimum widths and spaces betweenstructures; improve the structural properties of the underlyingdielectric insulator (e.g. low-k); and minimize or limit the impact onelectrical performance.

To provide a more computationally efficient method of data storage, thistechnique may also use a library of cells for placement of the dummyfill structures. Since the fill patterns may be repeated throughout thechip, the use of cells reduces the need to store redundant informationabout the dummy structures each time a region needs to be filled.Different size cells can be chosen from the library to fill a givenarea. A method that uses hierarchy of cells may also be used.

Implementation examples are described below. In the interest of clarity,not all features of an actual implementation are described in thisspecification. In the development of any such actual embodiment,numerous implementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related orbusiness-related constraints, which will vary from one implementation toanother. Moreover, such a development effort might be complex andtime-consuming, but would nevertheless be a routine undertaking forthose of ordinary skill in the art of having the benefit of thisdisclosure.

A dummy fill system is described that minimizes the impact of processvariation and subsequent electrical impact by embedding both amanufacturing process model or simulation and an electrical performancemodel or simulation inside the dummy fill method. The preferred methodforms a feedback system that determines the proper sizing and placementof dummy fill to minimize the impact of process variation on electricalperformance.

The following paragraphs describe an embodiment of the method, which isdepicted in FIG. 7. Sub-blocks (31, 33, 34 & 35) within FIG. 7 will bedescribed in greater detail in sections b. through f. An IC design iscommonly represented electronically (e.g. in a GDS format) in a libraryof files that define structures and their locations at each level of anintegrated circuit, 30. These files are typically very large in size,although the features that are relevant to process variation can bedescribed more efficiently. Layout extraction, 31, involves summarizingdiscrete grids of IC designs in a compact set of such parameters such aslinewidth, linespace and density for each grid. The layout features aremapped, 33, to wafer quality, such as film thickness, or electricalparameters, such as sheet resistance or capacitance. This informationmay be used with a process model (e.g. CMP) or set of process models(e.g. ECD and a multi-step CMP process or a more complex process flow)to predict or simulate the manufacturing results and correspondingvariation, 33-1. This variation can be measured physically, such asoptical measurement of the film thickness, or surface profiling of wafersurface to measure topography (e.g. dishing or step height and erosionor array height). The variation can also be measured electrically, suchas sheet resistance or capacitance, 33-2 and may require the use of theoriginal IC design, 39. The computed parameters from 33-1 and 33-2 areassembled for the full-chip, both within die and for multiple diesacross the wafer, 33-3.

Using a combination of both process models and electrical simulations,the performance of a given IC design can be predicted and comparedagainst the desired wafer quality and electrical parameters as well asdesign rule criteria, 32. In a mathematical sense, one could considerthis comparison to be a cost function, 35, based on reduction of processvariation while maintaining electrical performance, which drives theoverall dummy fill strategy.

If the design does not meet the specified tolerances, then dummy fill(either copper or oxide) may be added to adjust the layout parameters(e.g. density) and reduce the variation. The layout, extracted features,post-process parameters, 38, and design rules, 37, are fed into thesizing and placement algorithm, 34, which determines the size of thedummy fill objects, the pattern of the objects and the location orplacement of the structures within the design. There are two primarycomponents of dummy fill sizing and placement: rule generation, 34-1,and the sizing and hierarchical placement, 34-2. Rule generationconverts the design rules and constraints to dummy fill guidelines. Eachdummy fill object can be placed within the chip design as an independentobject but that approach increases the layout file size dramatically. Alibrary of hierarchical cells or meta-objects is created based onstructural features, such as linespace and linewidth. These cells can berepresented more efficiently in terms of file size and memoryrequirements, when placed hierarchically within the design file. Thesystem outputs a design file in a graphical computer aided design format(e.g. GDS). The complete system may be ran as each IC component is addedto the design, in real-time, or after all the components have been addedto the IC design layout. Our method iterates until a dummy fill strategyis determined that meets the desired process specifications andelectrical performance. The design is then certified for manufacturing,36.

Illustrative embodiments of a method for manufacturing are described inthe following sections. Section a. describes a use of an embodiment toreduce variation due to electroplated copper deposition (ECD)processing. Section b. describes the extraction of layout parametersrelated to process variation as a method to transform the large designfiles into a manageable set of features. Layout extraction is notrequired but is preferred. Section c. describes the preferred use ofprocess and electrical models to characterize the impact of processvariation on electrical performance. Section d. describes the use ofcost functions to measure the impact of dummy fill modification (or lackthereof) and how these functions may be used to achieve desired waferquality and electrical performance criteria. Section e. providesdetailed descriptions of the dummy fill rule generation and management,dummy fill sizing and dummy fill placement. Section f. describes, indetail, the hierarchical cell placement algorithm and the memorybenefits over non-hierarchical approaches. Section g. describes severalapplications of the described dummy fill system to damascene processflows, electrochemical deposition (ECD) and electrochemical mechanicaldeposition (ECMD) and integration of low-k dielectrics into damasceneprocess flows. Section h. describes the construction and computationalframework used to implement the dummy fill methods as well as theoperation of the dummy fill system and methods by users. Section i.concludes with results of the current implementation of the dummy fillmethods as well as screenshots of the user interface.

a. Use of Dummy Fill to Reduce Variation Related to ECD

Electroplated copper deposition (ECD) is used to create the interconnectstructures in a copper damascene process. The goal is to completely fillthe trench region in a void-free manner while minimizing the variationin the deposited copper thickness and minimizing the surface topography(often referred to as step height).

The time to closure (time it takes to completely fill the trench,described as time T_(f)) depends heavily on the width of the line andthe depth of the trench. For large trenches, the deposition of copper onthe sidewalls is small relative to the width of the trench. Therefore,these trenches tend to fill from the bottom up, often at the same rateas the deposition in the field region. The trench is thus filled withthe same copper thickness as that on the field, leaving a large step, orstep height, on the surface of the copper film over the wide damascenetrench. In contrast, the deposition of copper on the sidewall of smalltrenches rapidly reduces the width of the damascene trench. Thisincreases the concentration of the accelerator agent dissolved insolution, which results in a rapid acceleration of the deposition rateon the bottom of the trench. The copper in the trench fills much morerapidly than in the field area until it fills the trench. Residentaccelerators in the copper over the small trench causes the acceleratedcopper deposition to continue the over the trench, resulting in theformation of a copper bump, or negative step height. The combination ofthese effects with the large variation in trench width across the chiplead to a large variation in copper thickness and step height across thechip.

Dummy fill or dummy slots may be used to minimize the variation in thedeposited copper thickness and surface topography. The addition of dummyfill to the surface topography could significantly reduce the variation.This reduction in variation could lead to significantly more uniformpolishing, thus reducing the need for dummy fill and dummy slotting toreduce CMP variation.

For example, filling trenches with dummy oxide structures, or slots, canreduce the thickness and step height variation resulting fromelectroplated copper deposition. FIG. 8 illustrates a use of oxide dummyfill for ECD. FIG. 8A shows the difference between the deposited copperthickness, 40, over narrow linewidths, 41, and the deposited copperthickness, 42, over a wide linewidth or trench, 43. FIG. 8B illustrateshow the addition of oxide dummy posts, 44, in the trench, 45, results ina deposited thickness, 46, equal to the thickness, 47, over the narrowlinewidths, 48. The oxide dummy posts act to reduce the effectivelinewidth of the trench. The addition of oxide dummy fill is equivalentto the removal of metal, which is also referred to as slotting. Thisfigure illustrates a use of dummy oxide slots within wide trenches sothat the deposition of the wide trenches behave more like smalltrenches, thus reducing the difference in deposited copper thickness andstep height. If the oxide slots are small compared with the trenchwidth, there is a small change in the electrical properties of the wideinterconnects. By embedding electrical simulations into the method, theaffect of these slots is calculated. These calculations are used todetermine the width and density of the slots placed in the lines. Inaddition, the electrical calculations are used to limit the amount ofslotting based on limits on electrical performance loss specified by thedesigner. The proper addition of oxide dummy structures reduces the stepheight variation before CMP, which should result in a more uniform CMPprocess.

b. Layout Parameter Extraction

A layout is a set of electronic files that store the spatial locationsof structures and geometries that comprise each layer of an integratedcircuit. It is known that process variation, that negatively impacts theplanarity of processed films, is related to the variation in spatialdensities and linewidths of a given design. To characterize thisrelationship, our preferred method uses layout extraction, wherelinewidth and density features are extracted spatially across a chipfrom the geometric descriptions in layout files. The extractedinformation may then be used to determine areas of the chip that exceeddesign rule criteria regarding designed linewidth and density.

The layout parameters used to compute dummy fill includes the effectivepattern density and linewidth. Although the dummy fill method works withextracted densities and linewidths, some embodiments utilize theextracted linespace, as well as linewidth and density.

The flowchart in FIG. 9 provides a detailed flow of the layoutextraction component shown in 30 of FIG. 7. In FIG. 9, the layout fileis transferred or uploaded to the dummy fill system, 31-1. The layout isdivided into discrete grids, small enough so that aggregate computationsof mean, maximum and minimum features are used to represent thestructures in the grid and still allow accurate dummy placement, 31-2.Typical grid size in current implementations is 40 μm×40 μm. The gridsare ordered or queued for processing, 31-3. One good embodiment is touse multiple processors to compute the grids in parallel, 31-4. A gridis selected, 31-5 and within that grid each object, 31-6 has thelinewidth of the object computed, 31-7. This process is repeated forevery object within that grid, 31-8. For each set of neighboring objectsthe maximum, minimum and mean linespace is computed, 31-9. The effectivedensity for the entire grid is then computed, 31-10. This process isrepeated for all the remaining grids, 31-11. Once all the grids areprocessed, the extracted features are re-assembled from the differentprocessors, 31-12. A table is then created and the maximum, minimum andmean linewidth, linespace and density are placed in it as well as themaximum, minimum and mean linewidth for the whole chip, 31-13. Theminimum and maximum linewidth is used to compute a range.

The linewidth range (M) is divided by the number of desired bins (N),31-14, to determine the relative size of each of the N bins. For examplethe first bin would be the minimum linewidth or small nonzero value Δ tothe linewidth (M/N) and continue until the N^(th) bin which will spanthe linewidth from minLW_(BinN)=(N−1)·(M/N) to maxLW_(BinN)=(N)·(M/N),which is also the maximum linewidth. There are three sets of bins, a setof bins for each of maximum, minimum and mean linewidth. Each grid isseparated into the appropriate bins according to its max, min and meanlinewidth, 31-15. A histogram is also created for each bin showing thedistribution of values within that bin, 31-16. This information isstored in the database and fed into process models, in particular ECDmodels, as well as the dummy fill rules generation, 31-17.

The maximum, minimum and mean linespace ranges are computed for the fullchip, 31-18. The linespace range (M) is divided by the number of desiredbins (N), 31-19, to determine the relative size of each of the N bins.For example the first bin would be the minimum linespace or smallnonzero value Δ to the linespace (M/N) and continue until the N^(th) binwhich will span the linespace from minLW_(BinN)=(N−1)·(M/N) tomaxLW_(BinN)=(N)·(M/N), which is also the maximum linespace. There arethree sets of bins, a set of bins for each of maximum, minimum and meanlinespace. Each grid is separated into the appropriate bins according toits max, min and mean linespace, 31-20. A histogram is also created foreach bin showing the distribution of values within that bin, 31-21. Thisinformation is stored in the database and fed into process models, inparticular ECD models, as well as the dummy fill rules generation,31-22.

The density range is computed for the full chip, 31-23. The densityrange (M) is divided by the number of desired bins (N), 31-24, todetermine the relative size of each of the N bins. For example the firstbin would be the minimum density or small nonzero value Δ to the densityvalue (M/N) and continue until the Nth bin which will span the densityfrom minLW_(BinN)=(N−1)·(M/N)+Δ to maxLW_(BinN)=(N)·(M/N), which is alsothe maximum density. There is one set of bins for density. Each grid isseparated into the appropriate bins according to its density, 31-25. Ahistogram is also created for each bin showing the distribution ofvalues within that bin, 31-26. This information is stored in thedatabase and fed into process models, in particular ECD models, as wellas the dummy fill rules generation, 31-27. Finally all the linewidth,linespace and density information are stored either in the database oron the filesystem for later use in process model prediction or dummyrule generation and placement, 31-28.

Dummy fill placement algorithm also requires direct knowledge about theexact coordinates of an existing layout object and its surroundingneighbors. The object dimensions (length and width) must be known aswell as the space to nearby objects in each direction. To determine thecoordinates of the dummy fill region, four steps are required. In thefirst step, the coordinates of a selected object are obtained todetermine its length and width. In the second step, shown in FIG. 10A,the space from the selected object (object A), 50, to its nearestneighbors is computed in all directions, object B, 51, object C, 52,object D, 53, and object E, 54. If there is more than one object in agiven direction, the space and range information must be computed. Forexample, FIG. 10B shows that there is only one object to the east of theselected object and the space to the object B, 56, from object A, 55, isequal to 50 μm. In FIG. 10C, there are two objects, 58 & 59, to the eastof the selected object, 57. In this case, there are two ranges forlinespace; the range from object A, 57 to the farther object, 59, has alinespace of 50 μm. The range from object A, 57 to the closer object,58, has a linespace of 10 um. In FIG. 10D, there are also two objects,62 & 63. However, in this case there are three ranges, with no objectsto the east of object A, 60, between y=15 μm and x=20 μm, and object Ahas a range to the grid boundary, 61, as well. Depending on thesurroundings, one or more space ranges are generated for each object. Ifthere are no objects until the edge of the selected block or grid, 61,the space can be set to the distance between the object edge to the gridboundary or neighboring grids, 61, can be searched until an object isfound (or the chip boundary is reached).

The third step uses the linewidth and linespace information to find thedummy fill rule, often from a rule table. Finally, the fourth stepcomputes the coordinates of the dummy fill region based on the dummyfill rule and the coordinates of the selected object and its neighbors.

An example of how an extraction table is used to represent the full-chipor die is shown in FIG. 11. The chip or die, A1, is segmented intodiscrete grids, A3, and the extraction procedure, described in FIG. 9,is used to compute the linewidth, A4, linespace, A5, and density, A6,for each grid element. FIG. 11 illustrates how the linewidth (LW),linespace (LS) and density values placed in an extraction table relateto the grid at (y,x) coordinate (1,1) and the grid at (y,x) coordinate(2,1). In many cases, the max, min and mean of the features within eachgrid are stored in the table as well.

c. Process and Electrical Models

The dummy fill method presented herein uses a process model or a seriesof models (i.e. a flow) to predict the manufactured variation inphysical and electrical parameters from an IC design. By characterizingthe process variation relative to IC structures, dummy fill can be addedto minimize the variation of physical and electrical parameters from thedesired values. This method is not dependent upon any particular type ofmodel or simulation. However it is generally accepted that each processtool has unique characteristics and thus a model needs to be calibratedto a particular recipe and tool. Thus, it is often common practice toprocess a given IC design to determine the impact of processing onphysical and electrical parameters and to develop or calibrate processmodels specific to a particular tool or recipe, as shown in FIG. 12A. InFIG. 12A, the actual product wafer, 64, is processed using a recipe, 65,on a particular tool, 66. The pre-process wafer measurements, 67, andpost-process wafer measurements, 68, are used to fit model parameters,69. One good embodiment is a semi-empirical model that characterizespattern dependencies in the given process. The calibration modelparameters or fitting parameters, 70, may be extracted using any numberof computational methods such as regression, nonlinear optimization orlearning algorithms (e.g. neural networks). The result is a model thatis calibrated to this particular tool for a given recipe, 71.

It has been shown that certain IC characteristics such as featuredensity, linewidth and linespace are directly related to variation intopography for plating, deposition and CMP processes. It has also beenshown that test wafers that vary these features throughout some rangeacross the die can be used to build a mapping from design parameters(e.g. linewidth, linespace, density) to manufacturing variation (e.g.film thickness, dishing and erosion) for a given tool and recipe. Testwafers are an attractive alternative for assessing process impact inthat they are generally less expensive to manufacture and one test waferdesign can be used to characterize any number of processes or recipesfor a wide range of IC designs. As shown in FIG. 12B, a test wafer canbe also be used to generate a calibrated process model or multipleprocess models or a process flow. The calibration model parameters maybe computed using the same method in FIG. 12A, as such the details willnot be repeated here. One difference is that the pre-processmeasurement, 74, may be conducted by the test wafer manufacturer andretrieved in an electronic form, such as via the internet, email, discor CD or paper form. The other difference is that the resultingcalibration, 78, normally spans a much larger range of linespace,linewidth and density features and thus is more applicable to a broadrange of devices.

More details regarding the use of test wafers in calibrating a processare provided in FIG. 13A. A test wafer die is shown, 79, is patternedwith a range of linewidth and linespace values, 80. The test wafer isprocessed (e.g. CMP, ECD or deposition) on a tool using a given recipe,81, and the resulting variation is measured across the chip, 83, using ametrology tool (e.g. film thickness, 84). This mapping may be considereda model that maps a wide range of linewidth and linespace values to aparticular film thickness variation for this tool and recipe. Thesemapping are useful for predicting process variation for new IC designswithout having to actually tape-out masks and process the design, asshown in FIG. 13B. Linewidth and linespace features (whose range fallwithin the range, 86, spanned by the test die & wafer) are extracted,85, from a new IC layout. The extracted linewidth and linespace featuresfor spatial locations across the chip, 86, are input into the mapping,87 & 88, and an accurate prediction of film thickness variation acrossthe chip, 89 & 90, can be acquired for a given tool and a given recipewithout actually developing expensive lithography masks and processingthe new IC design.

As shown in FIG. 13C, the predicted process variation, 91, can be fedinto electrical models or simulations, 92, to assess the impact ofprocessing on the electrical performance of the chip, 93. The layout forthis design may be modified (e.g. through the addition of dummy fill ormodification of the design), new layout parameters extracted andevaluation of process variation repeated. This may be repeated until aparticular layout yields a desired level of process variation.

The following paragraphs and figure descriptions provide a detailed flowof an example of the use of process and electrical models tocharacterize variation, as implemented for dummy fill.

FIG. 14 describes the steps involved in calibrating a process model to aparticular tool or recipe. As described in FIG. 7, 1, layout extractionparameters are computed or in the case of test wafers uploaded from thewafer provider. The second step, 33-4-1 pre-measures the wafer usingmetrology equipment. These measurements may include film thickness andprofilometry scans to acquire array and step heights. The third step33-4-2 processes the test wafer for the particular process or processflow that is to be characterized. Such processes or flows may includeplating, deposition and/or polishing steps. The preferred method is tocalibrate on individual processes and also calibrate on sections of theflow as a way to best capture any coupling of variation betweensubsequent process steps in a flow. It is also recommended to calibratethe model for different recipe parameters such as time. The processedwafers are measured, 34-3-3 at the same locations as thepre-measurements; such measurements may include film thickness,profilometry or electrical, and the variation for the given process maybe characterized, 33-4-4. Process models or representations are uploadedin 33-4-5 and the pre and post measurements as well as computedvariation may be used to calibrate or fit the model or representation toa particular tool and/or recipe or recipes. These models may beformulated and uploaded by the user or selected from a library of modelson the dummy fill computer system. The pre and post measurements andcomputed process variation is used to fit the model or simulationparameters for the given tool and recipe, 33-4-6. The result, 33-4-7, isa process model calibrated to a particular tool and recipe or recipes.The result may also include a series of calibrated process models thatcan be used to simulate a process flow.

FIG. 15 describes the steps involved in using calibration models topredict the impact of process variation and subsequent variation inelectrical parameters and performance. A new layout or set of layoutfiles as well as desired IC features, geometries and design ruleinformation are loaded into the system, 30. The second step performslayout extraction, 31, to extract a description or set of featuresrelevant to process variation for a number of locations across the chip.One common approach is to discretize the layout into a number of gridsand a structure density is computed for each grid element. However, ourapproach computes the effective linewidth and linespace for each gridelement as well. The calibrated process models are uploaded or assembledto simulate processing, 33-4. The extracted layout parameters for eachspatial location are fed into the model and the resulting processparameters are computed, such as film thickness, dishing, erosion, arrayand step heights, 33-1. The difference between the target and predictedIC parameters are used to compute the process variation. The predictedprocess parameters may also be fed into electrical models or simulationsto characterize the electrical performance of the IC which when comparedwith the desired performance allows for the electrical variation to becomputed, 33-2. Some of the electrical parameters that may be computedinclude variation in sheet resistance, resistance, capacitance,interconnect RC delay, voltage drop, drive current loss, dielectricconstant or crosstalk noise.

Since this dummy fill algorithm is particularly suited for dummy filladjustments to interconnect layers, interconnect metrics (R,C,Lvariation) are used as general metrics for all areas of the chip, asshown in the following table. Other critical areas may requiresimulating the circuit performance effects of adding dummy fill. Forexample, a metric for the signal delay variation may be imposed inaddition to a percentage RC variation to ensure that timing constraintsof the critical paths meet the circuit specifications. Similarly, clockskew and crosstalk noise simulations may be used to determine whether ornot the circuit will function properly. This way, RC (or RLC) criteriacan be used as a first pass estimate of where to add the dummy fill.Then the dummy fill placement can be fine tuned in the next iteration byselectively performing circuit simulations for specific signals orcertain areas of the chip.

TABLE 1 Electrical performance metrics for dummy fill adjustmentPerformance Metric Metric Type Application Resistance (R) InterconnectECD, oxide dummy fill Capacitance (C) Interconnect EGD, oxide dummyfill, metal dummy fill Inductance (L) Interconnect High frequencies(ECD, oxide & metal fill) Signal Delay Circuit Routing, Buses, CriticalPaths Skew Circuit Clocks Crosstalk Noise Circuit Low swing/noisesensitive circuits

The result of models and simulations described in this section is afull-chip prediction of process and electrical parameters andperformance for a new IC design, as well as prediction of how theseparameters may improve as dummy fill is added, 33-3.

d. Dummy Fill Algorithm Cost Function

A cost function, 35, is used to measure how well an initial IC design ora given dummy fill scheme achieves the desired film thickness andelectrical parameters.

While film thickness variation is a universal concern, electricalperformance metrics may vary between technology generations and designgroups. As described in section c., interconnect metrics (R,C,Lvariation) can be used as general metrics for performance in all areasof the chip. Other critical areas may require simulating the circuitperformance effects of adding dummy fill. For example, a metric for thesignal delay variation may be imposed in addition to a percentage RCvariation to ensure that timing constraints of the critical paths meetthe circuit specifications. Similarly, clock skew and crosstalk noisesimulations may be used to determine whether or not the circuit willfunction properly. Similarly, voltage drop and drive current loss mayalso be used to determine the whether or not the circuit will functionproperly. Similarly, dielectric constant or effective dielectricconstant may be used in conjunction with low-k materials to determineeffects on capacitance. This way, RC (or RLC) criteria can be used as afirst pass estimate of where to add the dummy fill. Then the dummy fillplacement can be fine tuned in the next iteration by selectivelyperforming circuit simulations for specific signals or certain areas ofthe chip.

The predicted or simulated electrical and film thickness parameters areverified against desired target parameters. This characterization of howwell a particular dummy fill placement meets the desired film thicknessand electrical performance specifications is normally performed usingsome form of cost function. A cost function can be as simple as a checkto see if a particular film thickness non-uniformity threshold isexceeded or it could be as complex as a quadratic function ofnon-uniformity and undesirable electrical effects that are to beminimized in a feedback type system. In that a good dummy fill methoduses process and electrical impact, a useful embodiment is a costfunction and it minimizes the following parameters:

-   -   Thickness Non-uniformity=function of (LW, LS, density)    -   Electrical Performance=RC∥Delay∥Skew∥Noise    -   Delay=function of (R, C, L, R_(tr), C_(L))    -   Skew=function of (R, C, L, R_(tr), C_(L))    -   Noise=function of (R, C_(coupling)/C_(total), L, R_(tr), T_(r),        l)    -   Where:        -   R=interconnect resistance        -   C=interconnect capacitance        -   L=inductance        -   R_(tr)=driver resistance        -   T_(r)=signal rise time        -   C_(L)=load capacitance        -   C_(coupling)=intra-layer coupling capacitance        -   C_(total)=total capacitance (coupling+overlap+fringe)        -   l=interconnect length

The cost is a quadratic error function U based on a weighted sum ofprocess (film thickness) non-uniformity and electrical performancevariation, where the electrical performance is taken as one or more ofthe following metrics: RC, delay, skew, noise.Error_(T)=(T _(target) −T _(actual))Error_(Ep)=(EP _(target) −EP _(actual))

U = (Error_(T)^(T) ⋅ K₁ ⋅ Error_(T)) + (Error_(EP)^(T) ⋅ K₂ ⋅ Error_(EP))where:

-   T_(target)=vector of desired film thickness measurements-   T_(actual)=vector of actual or predicted film thickness measurements-   EP_(target)=vector of desired electrical performance metrics-   EP_(actual)=vector of actual or predicted electrical performance    metrics-   Error_(T)=column vector of film thickness errors-   Error_(EP)=column vector of electrical performance metrics-   U quadratic error, a scalar value, to be minimized-   K₁=Diagonal matrix with weights for 1 through q total film thickness    measurements along the diagonal elements.

$K_{1} = \begin{bmatrix}w_{T1} & 0 & 0 \\0 & ⋰ & 0 \\0 & 0 & w_{Tq}\end{bmatrix}$K₂=Diagonal matrix with weights for 1 through p total electricalperformance metrics along the diagonal elements.

$K_{2} = \begin{bmatrix}w_{EP1} & 0 & 0 \\0 & ⋰ & 0 \\0 & 0 & w_{{EP}_{p}}\end{bmatrix}$

The cost may encompass each signal line or a section of the chip thisway and the film thickness vectors and weighting matrices can be easilymodified to provide the correct quadratic error to be minimized over theentire chip. (One way is to concatenate them into a large vector of filmthickness and another as a large vector of electrical parameters;adjusting the weighting parameters appropriately). Another way is tohave separate error functions U for different areas of the chip that areweighted using a planarization length kernel function. There is normallysome tuning of the weighting parameters based upon those elements wherethe need to be minimized is greatest. This tuning may be automated orthe user may be prompted for a weighting scheme.

e. Dummy Fill Sizing and Placement

Although all the components in FIG. 7, have an impact on where dummyfill is placed, the actual decisions regarding the sizing and placementof dummy fill within the layout are performed in the component labeledas 34. Information about the process technology to be used is providedfirst, along with acceptable design variation criteria, to generate aset of metal and oxide dummy fill rules. A detailed flow diagram of thesteps in formulating the dummy fill rules, 34-1 is provided in FIG. 16.The process technology information may include the nominal values of themetal thickness T, inter-layer dielectric (ILD) thickness H above andbelow the metal layer of interest, the dielectric constant ∈, andconductivity ρ of the metal layer. The design criteria may include theacceptable percentage of tolerance in the interconnect RC delay and/orcrosstalk noise. These parameters, 38, and the layout and extractedlayout parameters, 37, are input into the dummy fill rule generationcomponent, 34-1.

Canonical interconnect structures, 34-1-1, are computed where an objector class of objects is uniquely identified or standardized within a setof features such as capacitance, linewidth, linespace or density. Atable is generated for each canonical interconnect structure found inthe design, 34-1-2. In the following loop (34-1-2 to 34-1-9), dummy fillrules are then generated for the combinations of linewidth and linespacespecified within a given range. This table is repeated for each of Jtotal metal layers within the design. If the technology changes, a newrule table is generated with the modified technology and designparameters.

Dummy fill rule generation begins with selection of the range oflinespaces and linewidths that span all the objects in a given layer,34-1-3. The electrical parameters (e.g. resistance (R) and capacitance(C)) and/or performance metrics (e.g. interconnect delay, voltage drop,drive current loss, dielectric constant or crosstalk noise) for all thelinewidth and linespace combinations are computed, 34-1-4. The percentvariation in electrical parameters is then computed for a range of dummyfill structures and sizes for all of the linewidth and linespacecombinations, 34-1-5. The maximum dummy size is selected, 34-1-6, thatmeets the percent variation tolerance levels, 34-1-8. Based upon allthese computations the dummy rule table is generated for each metallayer in the design, 34-1-7. The flow continues until all metal layersare computed, 34-1-9.

A sample dummy rule table is provided in FIG. 17. Maximum dummy filllinewidth (or available width of fill region) as a function ofinterconnect linewidth and linespace is shown. Dummy fill rules arebased on interconnect linewidth, width of available fill region andelectrical criteria (e.g. maximum capacitance variation of 5% with theaddition of fill). This sample rule table is for metal dummy fill. A newtable must be generated for each metal layer and changes as a functionof the technology parameters and performance metric/criteria.

The detailed flow diagram for the dummy fill sizing and placementalgorithm operations, 34-2, is described in FIG. 18. The metal and oxidedummy fill algorithm then takes the fill rules, 34-1-7, design rules,37, along with the layout file, extracted pattern densities, and the CMPprocess variation models, 38. The local pattern densities are extracted,34-2-1, with the aid of computer-aided design (CAD) tools or functionlibraries that obtain information about the features within the layout.Object coordinates are obtained and the area of each object within thelayout is computed. The pattern density is then computed for a smallsquare window, typically of side length 40 μm to 100 μm. The effectivepattern density is computed inside the dummy fill algorithm for each ofthe grids, also referred to here as a block, using an elliptical windowset to the planarization length L, associated with a given process,34-2-2. For each block, the predicted film thickness variation iscomputed, 34-2-3. Adaptations of this algorithm may also include thevariation in electrical parameters as well. The block fill priority isassigned, 34-2-4, based on the predicted film thickness non-uniformityby the process model, which is a function of the effective patterndensity, and in a damascene CMP process, also the linewidths andlinespaces of the patterned structures.

In performing the dummy fill, a block is selected one at a time based onits priority, determined by the non-uniformity in film thickness withinthat block, 34-2-5. Other physical layout parameters such as thelinewidths and linespaces are computed for all objects within theselected block, 34-2-6. This algorithm allows for some flexibilityregarding the use of a single dummy object size and pattern or selectionamong various sizes and shapes depending upon dummy fill rules.

Once dummy fill has been placed in a given block, layout extraction isperformed to update density, linewidth and linespace parameters, 31,which are used to compute process and electrical parameter variation isrecomputed, 33. The cost function, 35, is used to verify that the dummyfill solution meets the desired criteria. If so, the next block isprocessed, 34-2-4, until all blocks are filled. The dummy fill algorithmoperates on each level of interconnect generating dummy fillmodifications in two layers, one for metal and the other for oxide, andcontinues for each level of interconnect. If the constraints are met forall the blocks, the design is certified, 36.

The dummy fill placement and sizing algorithm shown as 34-2-8 in FIG. 18is described in detail, in FIG. 19. An object is selected in the blockwhere dummy fill is to be added, 34-2-7-1. A CAD tool is used to obtainthe bounding box coordinates for the selected object, 34-2-7-2. Theobject length and width is calculated, 34-2-7-3 and the distances tonearest neighbors in every direction are computed, 34-2-7-4. The type ofdummy fill, for example metal or oxide, is computed for each object,34-2-7-5.

For each object in the block, dummy structures are added, 34-2-7-6,where possible, in accordance with two constraints:

-   (1) Non-uniformity is greater than the design criteria-   (2) Dummy fill is performed based on the dummy fill rules tables.

The dummy fill rules may also determine which dummy fill size andpatterns are best for a particular object with regard to neighboringstructures. FIG. 20 provides two tables with sample dummy fill rules todetermine fill size and pattern. The first table, FIG. 20A, provides anexample of a dummy rule table used to adjust dummy fill size. LW refersto linewidth, LS refers to linespace, R refers to resistance and C tocapacitance. The second table, FIG. 20B, provides an example of a dummyrule table used to adjust dummy fill patterns

A number of dummy fill object sizes and patterns may be assembled in adummy fill library, 34-2-7-10, which can be modified as new ICtechnologies are designed. The user may also choose to override thealgorithm selection of size and patterns and include the chosenparameters within the technology design rule submitted at the beginningof the algorithm.

The dummy fill rules check for available area near the selected object,keeping an acceptable distance from the object based on the metal dummyfill rule. The oxide dummy structures are added based on similarelectrical rules. If either of these constraints is violated, no fillstructures are added for that object, 34-2-7-7. If there are noviolations then the dummy fill may be placed hierarchically as a moreefficient method to adapt the layout, 34-2-7-8. If layout file size isnot a concern, then each dummy fill object can be added directly to thelayout, each with it's own coordinates. There is a check, 34-2-7-9, tosee if there are any additional objects within the block and if so, thisprocess continues. If there are any additional objects within the block,the next object is selected, 34-2-7-1, and the process continues untilall objects within the block are processed.

FIG. 21 illustrates how two different dummy fill patterns, FIG. 21A andFIG. 21B, can have different fill object sizes but have similar density,thus illustrating how size provides another degree of freedom in dummyfill adjustment. As such, the dummy fill system may prompt the user toeither select fill type (grounded or floating), size and shape of fillpatterns from a library or alternatively, the sizing algorithm,automatically chooses the fill structure based upon dummy fill rules.

f. Hierarchical Dummy Fill Cells and Cell Placement

For process uniformity (e.g. CMP and ECD) as well as electrical effects(e.g. minimizing capacitive coupling across interconnect), dummy fillregions typically contain several small objects. The disadvantage ofplacing several dummy fill objects on a chip is that the file size canincrease significantly. In our approach, instead of placing severalsmall dummy fill objects across the chip, cells are of various sizes arecreated. This method requires the extra overhead of generating a celllibrary. However, once a cell library is generated, the only increase infile size resulting from the addition of dummy fill is in cellplacement. Additionally, the overhead in generating the cell library canbe reduced by creating the cells hierarchically. Although not required,this method is highly preferred for computational and storageefficiency. This method is performed during the placement of cellswithin the layout, 34-2.

FIG. 22A shows a cell that contains two dummy fill objects of size 1μm×1 μm separated by a space of 1 μm, 94. Since this cell has one rowand two columns, it is referred to as a cell of size 1×2. To create acell of size 2×2, another 1×2 cell is placed on top of the existing 1×2cell to create a new cell, 95. Similarly, a cell of size 4×4 can beformed by the following steps (shown in FIG. 22B):

-   Step 1. Create a cell of size 1×2, 96-   Step 2. Place another cell of size 1×2 to the right of the original    to create a 1×4 cell-   Step 3. Place another cell of size 1×4 on top of the existing 1×4    cell (2×4 cell created)-   Step 4. Place another cell of size 2×4 on top of existing 2×4 cell    (4×4 cell created)

The cells are created hierarchically, so that starting with the toplevel cell (4×4 cell in this case) and descending though each level ofthe hierarchy results in smaller cells. This occurs until the final cellis reached, which contains the actual dummy fill structures. Theadvantage of this approach is that the file size used to store the dummyfill information is significantly reduced, especially in large emptyareas where dummy fill is added. Rather than storing the coordinates ofeach dummy fill structure, only the cell size and cell coordinates needto be stored.

The hierarchical method results in a large reduction in file size, aswell as a much faster time to read the file in a layout editor. Forcomparison; let one unit represent the amount of memory it takes tostore the coordinates of a single dummy fill object or cell. The amountof memory required is about the same as for a single object since theyboth require the same amount of information:

For a single dummy fill object: bounding box coordinates (x₁, y₁; x₂,y₂)

For a cell: lower left coordinates (x₁, y₁; cell size m×n)

If the cells are placed hierarchically, a 4×4 cell will require 8 unitsof information, as illustrated in FIG. 23. In this example, each 4×4cell consists of two 2×4 cells, 96, each 2×4 cell consists of two 1×4cells, 97, each 1×4 cell consists of two 1×2 cells, 98 and each 1×2 cellconsists of two individual cells, 99. The total cells or objectsrequired for the 4×4 cell is computed to be 8 units of information. Fora non-hierarchical approach, the 16 individual cells would require 16objects to describe the four bounding box coordinates for eachindividual cell.

In general, the amount of savings by using the hierarchical approachincreases as the cell size (i.e. the area available for dummy fill)grows. The amount of storage required for an n×n cell without using thehierarchical approach is n²·4, where there are four coordinates used tospecify each fill object. With the hierarchical method used in thisdummy fill system, the amount of storage required is linear with n. Ingeneral, the amount of storage required for an m×n cell is always linearwith n (or m), and is equal to 4 (the amount needed to represent asingle cell) if m and n are powers of 2.

A flow diagram describing step-by-step details of the aggregation andplacement of hierarchical dummy fill cells is provided in FIG. 24. Thedummy fill rule table, 44-2-7-6 and fill object library, 34-2-7-10, areinput into the placement algorithm. Each dummy fill region is selected,34-2-7-8-1, and a computation is performed to determine the number ofdummy fill objects of a given size that can be placed within the fillregion, 34-2-7-8-2. The input parameters, 34-2-7-8-3, that define dummyfill parameters are used to generate, 34-2-7-8-4, a dummy fill celllibrary, 34-2-7-8-5, of various size cells that is made available to thecell placement algorithm, 34-2-7-8-6, that selects the largest size cellthat fills the selected region and places it, 34-2-7-8-7. The remainingdummy fill area is divided into new regions, 34-2-7-8-8, and thealgorithm determines if there is area still available for fill,34-2-7-8-9. If yes, then a new region is selected, 34-2-7-8-1, and thecell placement process repeated. If no, then dummy fill cell placementis complete and a check is done if additional objects require fill,34-2-7-9, FIG. 19.

g. Applications

The dummy fill methods described in this process are most applicable topolishing and electrochemical deposition processes where maintaining acertain level of film thickness uniformity is critical.

Dummy fill insertion may be used in conjunction with damascene processflows to improve film thickness uniformity for bulk copper fill usingelectrochemical deposition (ECD) and chemical mechanical polishing (CMP)where the two processes are used together during the creation of one ormore interconnect levels. Pattern dependent models of ECD and CMP may beused to characterize multi-level effects between adjacent interconnectlevels. This application to damascene process flows may be used over anetwork (internet, extranet or intranet) or as a web service to provideany of the following functionality:

-   -   layout extraction,    -   pattern dependent model calibration and prediction,    -   dummy fill sizing and placement into IC designs,    -   film thickness uniformity optimization and    -   electrical impact minimization        with the intent to improve film thickness uniformity or        electrical performance for either a ECD or CMP process or a        process flow that includes both.

In the application to damascene process flows, ECD and CMP process stepsare calibrated using the methods illustrated in FIG. 14. A new IC layoutis extracted using the steps and flow shown in FIG. 9 and described insection b. Calibration occurs as described in FIG. 14 for each process.The calibrated models are assembled as a process flow and used topredict the step-by-step and final film thickness uniformity using thesteps described in FIG. 15 and in section c. The results are examined incomparison with desired film thickness and electrical properties usingeither a table-lookup and threshold check or using a cost function, asdescribed in section d. The dummy fill algorithm is applied using thesteps shown in FIG. 16 through FIG. 24 and described in sections e. andf. This approach could be used to add dummy fill for each level,separately, to minimize film thickness non-uniformity or variation inelectrical parameters such as resistance, sheet resistance, voltagedrop, drive current loss, dielectric constant or capacitance. Thisapproach could also be used with multi-level models, that includeinteraction between layers, to generate a single dummy fill strategy formultiple interconnect levels.

For 130 nm, 100 nm and 65 nm technology nodes, dummy fill methods mayalso be applied to new processes to better enable process integrationand improve film thickness uniformity. Most conventional bulk copperfill is done using electrochemical deposition where various chemicalschemes that use additives, such as accelerators, leveler orsuppressors, are used to improve planarity of metal film. Equipmentmakers are also looking to mechanical approaches to improve uniformity.NuTool has proposed such an approach that rotates the wafer and uses apad to introduce the electroplating solution [patents 6,7 & 8]. Anadvantage of this contact plating technology is that the plated Cu filmmay be both locally and globally planarized. Another advantage is thatthis approach results in a thinner overburden of Cu that reduces theamount of CMP required in the next process step.

Dummy fill materials may be used in conjunction with an electrochemicalmechanical deposition (ECMD) process to improve film thicknessuniformity across the full-chip. Dummy fill methods may be used withECMD processes, including that described in [patent 6], by calibrating afull-chip ECMD model, using the flow described in FIG. 14, and insertingthe model into the process flow, 33-1, to acquire full-chip predictions,FIG. 15. Improvements in film thickness uniformity, dishing and erosioncould be achieved using the dummy fill methodology with an ECMD modeldeveloped by NuTool [patents 6,7 & 8], internally or by some other thirdparty.

This application may utilize network (internet, extranet or intranet)based applications and web services to provide any of the followingfunctionality:

-   -   layout extraction,    -   pattern dependent model calibration and prediction,    -   dummy fill sizing and placement into IC designs,    -   film thickness uniformity optimization and    -   electrical performance optimization        with the intent to improve film thickness uniformity for ECMD        processes.

There are several challenges for introducing low-k dielectrics into adamascene process flow. It not only difficult to create a quality low-kfilm but also to maintain the dielectric constant after all theintegration steps such as etch stop layers and barrier caps on copperand CMP stop layers. Many low-k yield problems are related to copper CMPwhere the softness of the low-k films results in CMP damage, dishing anderosion and subsequent electrical defects.

Dummy fill materials may be inserted in low-k films to adapt structuralproperties of these films with the intent of achieving a desiredeffective dielectric constant and reducing capacitance spatially acrossthe full-chip when integrated into an interconnect process flow. Patterndependencies may be characterized relative to changes in the effectivedielectric constant (including the use of wafer-state models andelectrical parameters). Test wafers may be used to develop full-chipmodels to predict variation in effective dielectric constant as afunction of film thickness uniformity, dishing or erosion.Characterizations of pattern dependencies may be used to automaticallyadd dummy fill directly into low-k films to minimize the variation ineffective dielectric constant when low-k materials are used ininterconnect levels. This application may utilize network (internet,extranet or intranet) based applications or access and use web servicesto provide or integrate any of the following functionality:

-   -   layout extraction,    -   pattern dependent model calibration and prediction,    -   dummy fill sizing and placement into IC designs,    -   film thickness uniformity optimization and    -   electrical performance optimization        with the intent to improve the structural properties of low-k        films and the development and integration of process steps using        low-k dielectric films.

This application may be used in conjunction with the dummy fill methodto alter the physical, structural and electrical properties of low-kdielectric films to facilitate planarization using CMP, as performed indamascene processes and as illustrated in FIG. 25. The steps forintroducing low-k films into a process flow are very similar to thegeneral method described in FIG. 7. This application would require thecalibration, 34, of models for ECD or ECMD and CMP for use with low-kdielectric films, as outlined in FIG. 14. This application also requiresadapting the electrical models, 33-2, to include computation of thevariation in the effective dielectric constant across the chip. Thedesired effective dielectric constant data along with other design ruleparameters, 32, could be input into the cost function to direct dummyfill strategies that optimize the electrical properties of the low-kfilm, while improving film thickness uniformity at the conclusion of thedamascene process.

h. Construction and Operation

The components that comprise the method are constructed in software(e.g. Java, Tcl, Basic, SQL) and modularized such that the method may ormay not use all the components in the placement of dummy fill. Forexample, the dummy fill library may consist of only one type of dummyfill object and the automated dummy fill algorithm may not require anelectrical model or simulation to optimally place dummy fill with regardto reducing process variation. The following descriptions will attemptto provide the general computational framework for the dummy fillmethods.

FIG. 26 provides the preferred software architecture used to constructthe dummy fill method and is described in the following paragraphs. Theuser, 100, communicates to the system through a graphical user interface(GUI) 101, such as a web browser. The GUI, 101, allows the user tochoose and upload electronic layout design files into the dummy fillsystem.

In general the GUI, as defined and used throughout this section, allowsthe user to choose, upload or transfer from another form of electronicmedia, the desired design rules and electrical performance for theparticular device described by the design files. The user may also usethe interface to select process and electrical models from a server ortransfer or load models from another electronic media source orcomputer. The user may also use the interface to select dummy fillshapes, sizes and patterns from a dummy fill object library residing onthe server or transfer or load models from another electronic mediasource or computer. The user may also use the interface to review theresults of dummy fill adjustments to the layout and/or view theresulting full-chip layout spatial densities, predicted process filmthickness and/or electrical parameters. These results may be in the formof:

-   -   histograms and other statistical plots,    -   full-chip images of wafer-state or electrical parameters at some        point in time,    -   movies of full-chip film thickness, dishing, erosion progression        during a process step or flow,    -   movies of full-chip electrical parameter variation such as sheet        resistance and capacitance,    -   and tables of values.

The GUI 101 communicates with a series of software components, servicesor functions 102 (referred to here as the service module) that managethe flow of information throughout the system to the database, 105, filesystem, 105, and computational core processes, 103, as well. Theservices, 102, are modular in nature and serve to initiate thecomputational core processes, 103, that execute portions of thealgorithm and to assemble and format the content for display in the GUI.Useful embodiments of these components are as Java or Tcl scripts whichenable easier interaction with the database using embedded SQL code andwith the GUI using HTML, XML or dynamic HTML interpretation. Thesecomponents also allow the ability to initiate mathematical processesthat perform the computation necessary to determine the correctplacement of dummy fill within the layout.

The service model, 102, communicates with the computational core ofprocesses and functions, 103, that execute the dummy fill algorithms andheavy computational processes such as the process and electrical modelsand simulations. This core does also does the effective pattern densitycomputation. This communication may include instructions, data, modelparameters, prediction results in tabular, image or movie forms andpointers to files in the file system.

The service module, 102, also communicates with electronic IC designsoftware, 104, to manipulate layout information such as the location andcoordinates of design objects and determine where to place the dummyfill cells.

The database, 105, communicates with the service module, 102, via SQLcommands to manage system data such as dummy fill library objects, userprofiles that specify permissions and preferred content andpresentation, user data which may include layout extraction data, priorlayout design files, model parameters for particular tools and processesand full-chip prediction results such as surface topology, resistanceand capacitance. Examples of databases that may be used include Oracle,Informix, Access, SQL Server and FoxPro.

The file system, 106, communicates with all the components 101, 102,103, 104 and 105 to retrieve and store information saved as files.

If the functionality shown in boxes A, 107, and B, 108, resides on onecomputer then the system is configured as stand-alone. If A and B resideon different computers and communicate across a network, the system isnormally considered a client-server configuration.

The intent in this section is to not describe all possibleinstantiations of the dummy fill method but provide a few preferredoperational frameworks. There are three basic computational frameworksdescribed in this section that constitute preferred methods of operationand delivery of the functionality based upon a user's needs. The firstframework presented is a stand-alone configuration, shown in FIG. 27A,where all the components (101-106 of FIG. 13) reside in 109 and areaccessed from a single computer. The second framework is a client-serverconfiguration, shown in FIG. 27B, where the GUI (101 of FIG. 37) resideson a client computer which accesses, via a network, 111, the othercomponents (102-106) residing on a server or multiple servers, a serverfarm, 110. The communication could be done via internet, intranet orextranet networks, 111, and the server may serve one or more clients orusers.

The third framework, FIG. 28, is an extension of the client-server modelthat includes communication via a network, 114, with additionalcomputers that may contain one of more components (115-120) of thesystem. For example, a design house may utilize the dummy fill methodvia the server, 118, but remotely utilize a separate computer whichhouses process models or model parameters, 117, that are provided by afoundry where manufacturing may be outsourced. This framework alsoincludes the use of third-party electrical models and simulations, 117,linked to the dummy fill method residing on a server or server farm,ref, 118, via a network connection, 114.

To scale the dummy fill methods to serve a large client base locatedthroughout the world, dummy fill services may be delivered via the webusing the client-server framework described in FIGS. 27B and 28. In thisoperational framework, the dummy fill methods and functionality aredeveloped as web services that are accessible through any web browser,located anywhere in the world that has internet access.

The general architecture, shown in FIG. 29, may be used with any dummyfill approach where dummy fill services are provided via the web. Inthis framework the user, 100, through a client computer, 121, accesses aserver computer or server farm, 123, which performs the dummy filloperation over a network (e.g. intranet, extranet or internet), 122.Using the web or a network connection, 122, the client, 121, uploads ortransfers the layout file or files to the server, 123, and may alsotransfer design rule and other preferences to be used by the dummy fillsystem on the server. The dummy fill system, 123, processes the layoutinformation, places dummy fill objects and returns the layout file tothe user, 100, via the web or network connection, 122.

A useful embodiment of this framework is to provide the dummy fillfunctionality in the form of web services. A web service is an objectthat exists as a function, content or a process that may interact with aweb-browser, database or other services. The web service architecture ispreferred in that it enables each dummy fill function and the contentthat is returned to the user to be modularly created and assembled andtailored to the user's needs and it allows the method to be more easilyscaled to a larger user base. Another benefit is that web servicesprovided by third-parties may be automatically accessed and integratedinto the dummy fill web application. Another benefit of this embodimentis that development of software-based services via the web allows thedummy fill algorithms to be scaled and supported efficiently for worldwide use.

The preferred form of these web services is as Java, Tcl or SQL basedsoftware programs that communicate with a SQL enabled database and acore of mathematical programs to manipulate the layout information anddetermine the proper dummy fill functions. These services specify orpublish what input parameters are required, which are optional and whatparameters and data are provided in return. The system integrates theseservices according to the user's permissions and the functionalityrequired.

A useful embodiment for dummy fill system is shown in FIG. 30. The user,100, working through a web browser based GUI on a local client, 124,communicates to the server, 126, via a network (e.g. internet, intranet,extranet), 125. Functionality provided by the dummy fill methods existsas modular and configurable web services, 128. The dummy fill webservices, 128, may reside in the service module, shown in 102 of FIG.26, and may be built on a web application platform provided by athird-party, such as those provided by IBM, Microsoft, ARSDigita or BEA.The server, 127, will likely include web services to manage user, 100,and the user's company profiles and permissions to tailor the contentand functionality available to the particular user logged in. Some ofthe web services may be assembled from elsewhere across a network andmay be published services by third-parties, that are assembled by acentral web server or server farm, 127. Some of the components that mayexist as web services are process models and simulations, electricalmodels and simulations (129), layout extraction (130), hierarchical cellplacement (134), dummy fill sizing and placement (132), dummy fillobject libraries (133) and design rule creation, modification orpublication (131). The server, 126, allows the user, 100, to build theirown web based dummy fill application tailored to a particular problem oruse, through the use of a wizard that prompts the user with questionsand then assembles the proper services from 128. Such uses of theseservices, 128, may also be provided over trial-periods at a discount orno-cost fee.

A meta-service or complete web-based application can be assembled from anumber of smaller services (or functions) according to the user's needs.This is why it is often beneficial to create very modular web servicesthat promote flexibility in the type of dummy fill application that isassembled. FIG. 31 illustrates how the service module in 102, may beused to tailor, configure and assemble a web-based dummy fill webapplication. A user from a particular company logs in, 135, and aservice script, 136, checks to see what permissions, 137, this user haswith regard to objects within the system such as: layout, tool andmeasurement data, dummy fill functions, purchasing authority. Thesepermissions are used to initiate creation of the dummy fill webapplication, 138. The service module continually acts on input from theuser, as they use the system. If that same user loads a particularlayout, 139, the service script, 140, retrieves all the functions andobjects associated with that type of layout as well as the calibratedprocess tool and models and recipes this user may have access to, 141.The service script assembles links to these permitted objects into thedummy fill web application, 142. The same user may select one or more ofthose process tools and recipes to create a process flow, 143. Theservice script, 144, then retrieves the correct calibrated models andassembles those models into a process flow, 145. The process flow thenbecomes part of the dummy fill web application, 146. This interactioncontinues as the user submits requests to the server and a serviceresponds, 147.

The dummy fill methods and system may be used by designers in a seriesfashion, as after all components are placed in the layout. In thisoperation or use, shown in FIG. 32, design specifications and rules,148, are submitted to designers, 149. The designers design and placetheir components, 150, resulting in a completed layout, 151. Thecompleted IC design, 151, is uploaded to the server, 152, and the dummyfill methods & system modifies the IC layout to the designspecifications returning it to the design group, 153. If there are nodummy fill strategies that can meet design specifications, the designersare notified, 154. This configuration could be used by a fab or foundryto provide dummy fill services and/or certify the manufacturability oflayouts submitted by design houses.

The dummy fill methods and system may be used by designers in aniterative fashion, as each component is placed in the layout (this modeis also referred to as real-time). In FIG. 33, the designspecifications, 155, are provided to the design group, 156. As thedesigners design and place each IC component, 157, the layout isuploaded to the server, 158, which operates on the layout as thedesigners place components, in real-time. To process the completelayout, even when only a few components have been placed, any non-placedcomponent is assumed to meet the design specifications. As the dummyfill method modifies the layout it is certified to the designspecifications and returned to the design team or electronically updatesa central layout used by all designers in the group, 159. If the dummyfill method cannot determine a dummy fill strategy that meets the designcriteria the design group is so noted.

The internet may allow for collaborative design among design groups indifferent companies, located across the world. The challenge is toensure that all designers can meet the design specifications. In thecase where design specifications are agreed to by all design groups, thesystem operates similar to FIG. 33—assuming non-placed components meetthe design specifications and adding dummy fill appropriately if thesecomponents violate specs when placed. In the case where componentsdesigned under different design rules (such as licensed IP or designs)may be chosen, the dummy fill system can determine whether a dummy fillscheme exists to allow the pre-designed component to be integratedwithin a new design specification.

i. Results

The dummy fill system described in this application has been implementedand the figures and test in this section describe some of our results.The graphical user interface (GUI) for the Layout Manager component,shown in FIG. 34, allows the user to upload a layout through a webbrowser and web services are automatically configured to add dummy fillfor the appropriate processes and according to user defined design rules(also input through a similar GUI). The three designs, 161, 162 & 163,were processed using the layout extraction algorithm to computeeffective density. Options are provided to the use to use our layoutextraction methods to compute linewidth and linespace or to upload thisinformation from another source, 164, 165 & 166.

The results of a layout extraction using the system are shown in theimages in FIG. 35. The spatial linewidths across the full-chip are shownaccording to which linewidth bin they fall into. This information isinput into the models to predict process and electrical variation.

Results of the dummy fill methods and system are provided in FIGS. 36and 37. In FIG. 36A, a metal line, 177, is shown adjacent to an oxidefield where metal dummy fill has been added, 176, using CMP filmthickness, dishing and erosion computation, 31, and resistance andcapacitance electrical models, 32, and is within acceptable RC variationtolerances. In FIG. 36B, oxide dummy fill, 178, has been added to ametal line, 179. A zoomed in insert is also shown, 180, where the actualoxide dummy fill structures can be observed, 181. The oxide dummy fillhas been added using CMP film thickness, dishing and erosioncomputation, 31, and resistance and capacitance electrical models, 32,and is within acceptable RC variation tolerances. The dummy fill systemalso uses dynamic line buffering and dynamic slotting percentages in theplacement of dummy fill in these results. FIG. 37 shows how the dummyfill method and system can be used to adjust the dummy fill pattern tominimize electrical impact. In FIG. 37, metal dummy fill, 184, is placedin an oxide field region adjacent to a metal line, 183. To meet theelectrical requirements for this design, an asymmetric metal fillpattern is chosen from the fill library and sized and placed to minimizethe impact of resistance in the oxide field region where it is added. Aninsert, 186, is shown where the asymmetric metal dummy fill pattern,187, can more easily be seen.

The GUI for using dummy fill services is shown in FIG. 38 and a usefulembodiment is to use a web browser as the GUI. The benefit being thatalmost every computer is now equipped with a web browser and there is agreat deal of standardization across the two major browsers fromNetscape and Microsoft. The dummy fill services and functions aregrouped within the GUI into three primary components; design (199),manufacture (191) and model (200). The screenshot in FIG. 38 shows inthe header, 190, and in the navigation bar, 191, that the manufacturecomponent has been selected by the user. Within the manufacturecomponent are subcomponents; fabs, tools, wafers and measurement dataand in this screenshot, tools, 192, have been selected. There are threesubcomponents under tools; types, recipes and flows. In this screenshotthe user has selected types, 193. The types of tools and tool settingsavailable to this user are shown, 194. The available recipes for thistool type, 196, and available recipe sequences, 197, for these tooltypes are shown. The system configured in this screenshot has twoprocess models available to the user, 198, for calibration andprediction of copper and STI CMP. The design component, 199, uses alayouts manager to allow the user to upload and manage layouts andlayout extractions. One goal of the dummy fill GUI design is to allowthe user to manage all the data and results associated with dummy fillservices provided.

Although various implementations have been discussed above, otherimplementations are also within the scope of the following claims.

1. A method comprising: enabling generation or placement of dummy fillfeatures within a design of an integrated circuit that is to befabricated using a fabrication process flow that includes one or moresteps including one or more chemical mechanical polishing process steps,the generation or placement of dummy fill features being based on (a)dimensional characteristics of dummy fill features and/or non-dummyfeatures within the design of the integrated circuit and (b) predictionsby a pattern-dependent model characterizing interaction of (c) thedimensional characteristics of the dummy fill features and/or thenon-dummy features with (d) a topography of material within theintegrated circuit that results from one or more steps of thefabrication process flow including at least one step of a chemicalmechanical polishing process, the step of the chemical mechanicalprocessing removing conducting or non-conducting material associatedwith features within the integrated circuit.
 2. The method of claim 1also including: operating a server to provide dummy fill generation orplacement functions for a semiconductor design, and enabling a user at aclient to operate through a web browser to develop the dummy fillplacement strategy.
 3. The method of claim 2 in which the server islocal to the user.
 4. The method of claim 3 in which the server isremote from the user.
 5. The method of claim 1 also including: analyzinga design to which the dummy fill strategy has been applied, adjustingthe design based on the analysis, iterating the analyzing and adjustingsteps, and certifying that an integrated circuit manufactured accordingto the adjusted design will be within predefined physical and electricalparameters.
 6. The method of claim 1 in which the one or more steps ofthe fabrication process flow comprise two or more processes.
 7. Themethod of claim 1 in which the one or more steps of the fabricationprocess flow comprise two or more steps of a single process.
 8. Themethod of claim 1 in which the one or more steps of the fabricationprocess flow comprise deposition and chemical mechanical polishing. 9.The method of claim 1 in which the generation or placement of dummy fillincludes generating dummy fill rules.
 10. The method of claim 1 alsoincluding: defining a set of hierarchical cell placements for dummyfill, and reducing a size of an electronic layout file to which dummyfill is added by using the hierarchical cell placements.
 11. The methodof claim 1 in which the dummy fill generation or placement is performedby a user through a web browser and a web server.
 12. The method ofclaim 11 in which the web server is local to the user.
 13. The method ofclaim 11 in which the web server is remote from the user.
 14. The methodof claim 1 in which the fabrication process comprises a damasceneprocess.
 15. The method of claim 1 in which the generation or placementof dummy fill includes determining the size and placement of dummy fill.16. The method of claim 1 in which the fabrication process comprises aformation of a low-K interlayer dielectric.
 17. The method of claim 1 inwhich the fabrication process comprises chemical vapor deposition orspin-on of the low-K dielectric.
 18. The method of claim 1 in whichgenerating the dummy fill strategy includes dividing a semiconductordesign into grids.
 19. The method of claim 18 in which the generation orplacement of dummy fill also includes extracting local pattern densitiesfor a semiconductor design for each of the grids.
 20. The method ofclaim 18 in which the generation or placement of dummy fill alsoincludes extracting local line width for a semiconductor design for eachof the grids.
 21. The method of claim 18 in which the generation orplacement of dummy fill also includes extracting local line spacing fora semiconductor design for each of the grids.
 22. The method of claim 18in which the generation or placement of dummy fill also includescomputing an effective pattern density for each grid.
 23. The method ofclaim 19, also including using models for computing film thicknessnon-uniformity with respect to a semiconductor design for which thedummy fill is being generated or placed.
 24. The method of claim 23 alsoincluding computing a variation in film thickness.
 25. The method ofclaim 18 also including deriving coordinates of all objects within eachof the grids.
 26. The method of claim 25 also including generating atleast one of line width, line space, length, and bounding box withrespect to each of the objects.
 27. The method of claim 18 in which thegeneration or placement of dummy fill includes adding dummy fill inempty areas of each of the grids.
 28. The method of claim 25 in whichthe dummy fill includes slots in objects.
 29. The method of claim 27comprising re-computing a local density in each of the grids afteradding dummy fill.
 30. The method of claim 27 also comprisingre-computing an effective pattern density for each of the grids afteradding dummy fill.
 31. The method of claim 1 in which the generation orplacement of dummy fill is based on criteria for electrical parametervariation tolerances for at least one of the following: capacitance andresistance, sheet resistance, outputs delay, skew, voltage drop, drivecurrent loss, dielectric constant or crosstalk noise.
 32. The method ofclaim 30 in which the effective pattern density is computed based on apolishing process planarization length.
 33. The method of claim 30 inwhich the effective pattern density is computed using an ellipticallyweighted window or other filter.
 34. The method of claim 9 in whichdummy fill rules based on electrical design guidelines are generateddynamically with a change in technology or design parameters.
 35. Themethod of claim 32 in which an effective pattern density is generateddynamically with a change in a process planarization length.
 36. Themethod of claim 1 in which the fabrication process compriseslithography.
 37. The method of claim 1 in which the fabrication processcomprises electrochemical deposition.
 38. The method of claim 1 in whichthe fabrication process comprises copper chemical mechanical polishing.39. The method of claim 1 also including; extracting patterndependencies from a layout of the semiconductor.
 40. The method of claim39 in which the layout dependencies include with respect to linespacing, line width or line density.
 41. The method of claim 1 alsoincluding: using patterned test wafers or test semiconductor devices tocalibrate a pattern dependent model with respect to a preselected toolor process recipe.
 42. The method of claim 1 also comprising: using thepattern dependent model to map pattern dependent features to wafer-stateparameters comprising at least one of resulting film thickness, filmthickness variation, dishing, erosion and electrical parameters such assheet resistance, resistance, capacitance, crosstalk noise, voltagedrop, drive current loss, dielectric constant and effective dielectricconstant.
 43. The method of claim 1 also comprising: using a costfunction to measure an impact of dummy fill modification on processinduced wafer state and electrical parameter variation.
 44. The methodof claim 1 also comprising: based on a combination of more than onepattern dependent model, generating a strategy for placement of dummyfill in a fabrication process, and predicting an impact of the dummyfill generated by the strategy on process variation.
 45. The method ofclaim 1 also comprising: based on a combination of more than one patterndependent model and cost function, generating a strategy for placementof dummy fill in a fabrication process that optimizes full-chipwafer-state and electrical parameters.
 46. The method of claim 1 alsocomprising: based on predicted or simulated wafer state and electricalparameters, generating dummy fill rules for use in dummy fill placementin a semiconductor fabrication process.
 47. The method of claim 46 inwhich the dummy fill rules include dummy fill sizing.
 48. The method ofclaim 46 in which the dummy fill rules include dummy fill placement. 49.The method of claim 46 in which the dummy fill rules include dummy fillhierarchical cell creation and management.
 50. The method of claim 1also comprising: providing dummy fill functions to generate the dummyfill strategy, and using the functions to automatically modifyGDS-format electronic layout files for a semiconductor device.
 51. Themethod of claim 1 also comprising: at an internet server, receiving froma client a layout file for a semiconductor device, generating dummy fillmodifications to the layout file at the server, and returning the dummyfill modified layout file from the server to the client.
 52. The methodof claim 1 also comprising: at a server, providing a service thatenables a user to interactively configure a dummy fill applicationrunning on the server, and enabling the user to generate dummy fillinformation using the dummy fill application.
 53. The method of claim 1also comprising: making available to a user on a network a service thatenables the user to verify dummy fill information with respect to asemiconductor design and a fabrication process.
 54. The method of claim53 in which the dummy fill information that is verified includes atleast one of a dummy fill pattern, a dummy fill strategy, or a dummyfill representation.
 55. The method of claim 53 in which the dummy fillinformation is verified with respect to a single interconnect level ofthe semiconductor design.
 56. The method of claim 53 in which the dummyfill information is verified with respect to multiple interconnectlevels of the semiconductor design.
 57. The method of claim 53 alsoincluding sizing dummy fill objects and creating a dummy fill pattern ofthe objects for one or more interconnect levels of the semiconductordesign.
 58. The method of claim 53 in which the dummy fill informationcomprises dummy fill rules.
 59. The method of claim 57 in which thepattern includes oxide or metal dummy fill objects.
 60. The method ofclaim 57 in which the objects of the dummy fill pattern are placed tominimize full-chip film thickness variation.
 61. The method of claim 57in which the objects of the dummy fill pattern are placed to minimizefull-chip variation in electrical parameters.
 62. The method of claim 61in which the electrical parameters comprise at least one of sheetresistance, resistance, capacitance, crosstalk noise, voltage drop,drive current loss, and effective dielectric constant.
 63. The method ofclaim 50 in which the GDS files are modified to improve uniformity andelectrical performance of the semiconductor device.
 64. The method ofclaim 63 in which the fabrication process comprises a damascene processflow.
 65. The method of claim 1 also comprising: making available to auser on a network a web-based application comprised of web services thatenables the user to verify dummy fill information with respect to asemiconductor design and a fabrication process.
 66. The method of claim1 in which the dummy fill placement or generation includes using dummyfill objects to improve a structural integrity of low-K dielectricfeatures.
 67. The method of claim 1 in which the dummy fill placement orgeneration includes using dummy fill objects to maintain or improve aneffective dielectric constant of low-K dielectric features.
 68. Themethod of claim 57 or 67 in which the effective dielectric constant ismaintained through all steps of a damascene process flow.
 69. The methodof claim 1 in which the dummy fill placement or generation includesusing dummy fill objects to facilitate integration of low-k dielectricmaterials into a damascene process flow.
 70. The method of claim 1 alsocomprising: maintaining a library of semiconductor dummy fillinformation, and making the library available for use in connection withgenerating dummy fill placement specifications, and updating the librarywith changed dummy fill information.
 71. The method of claim 1 alsocomprising: storing calibration information with respect to at least oneof the following: fabrication process tools, recipes, and flows, andupdating the calibration information to reflect changes in thefabrication process tools, recipes or flows.
 72. The method of claim 71also including using the calibration information in generating orplacing dummy fill.
 73. The method of claim 71 also including selectingamong fabrication process tools, recipes and flows from calibrationdatabase based upon desired dummy fill characteristics.
 74. The methodof claim 1 also comprising: enabling a user to obtain a dummy fillstrategy for a semiconductor design using a single click of a userinterface device through a user interface.
 75. The method of claim 1also comprising: enabling a user to obtain a dummy fill strategy for asemiconductor design over the internet using web services.
 76. Themethod of claim 1 in which the pattern dependent model predicts resultsof applying the chemical mechanical polishing process to fabricateintegrated circuits having an arbitrary generated pattern.
 77. Themethod of claim 1 in which the dimensional characteristics comprise oneor more of width, density, thickness variation due to underlyingtopography, and the topography of one or more materials formed in one ormore steps in one or more levels of the integrated circuit.
 78. Themethod of claim 1 in which the one or more steps of the fabricationprocess flow include one or more individual steps within an interconnectlevel of the integrated circuit or multiple steps within one or moreinterconnect levels of the integrated circuit, including at least onestep of a damascene process, the step including a step of CMP or ECD.79. The method of claim 1 in which the conducting or non-conductingmaterial being removed includes one or more of copper, dielectric,barrier.